Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks

Park, Namyong; Kan, Andrey; Dong, Xin Luna; Zhao, Tong; Faloutsos, Christos

doi:10.1145/3292500.3330855

Cited by 132 publications

(65 citation statements)

References 15 publications

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“…We used four publicly available real-world KGs that have different characteristics, and were used in a previous study on node importance estimation [19]. We constructed these datasets following the description in [19]. Below we give a brief description of these KGs.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…We use the following baselines: PageRank (PR) [17], Personalized PageRank (PPR) [8], HAR [15], and GENI [19]. PR, PPR, and HAR are representative random walk-based algorithms for measuring node importance.…”

Section: Baselinesmentioning

confidence: 99%

“…To leverage edge type information for node importance estimation, HAR [15] employs a signal propagation schema that is sensitive to edge types. Recently, with the advent of deep learning on graphs, a graph neural network-based model, GENI [19], was developed. By using graph attention mechanism, GENI adaptively fuses information from different edge types, and achieved the state-of-the-art results for node importance estimation.…”

Section: Related Workmentioning

confidence: 99%

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MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals

Park

Kan

Dong

et al. 2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Given multiple input signals, how can we infer node importance in a knowledge graph (KG)? Node importance estimation is a crucial and challenging task that can benefit a lot of applications including recommendation, search, and query disambiguation. A key challenge towards this goal is how to effectively use input from different sources. On the one hand, a KG is a rich source of information, with multiple types of nodes and edges. On the other hand, there are external input signals, such as the number of votes or pageviews, which can directly tell us about the importance of entities in a KG. While several methods have been developed to tackle this problem, their use of these external signals has been limited as they are not designed to consider multiple signals simultaneously. In this paper, we develop an end-to-end model MultiImport, which infers latent node importance from multiple, potentially overlapping, input signals. MultiImport is a latent variable model that captures the relation between node importance and input signals, and effectively learns from multiple signals with potential conflicts. Also, MultiImport provides an effective estimator based on attentive graph neural networks. We ran experiments on real-world KGs to show that MultiImport handles several challenges involved with inferring node importance from multiple input signals, and consistently outperforms existing methods, achieving up to 23.7% higher NDCG@100 than the state-of-the-art method. CCS CONCEPTS • Information systems → Data mining; • Computing methodologies → Neural networks.

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Section: Dataset Descriptionmentioning

confidence: 99%

Section: Baselinesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals

Park

Kan

Dong

et al. 2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

Self Cite

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“…PPR (personalized pagerank) [92] 是另外一类基于图模型的重要性评估算法, 但不同于 PR 算法, PPR 允许用户对图模型中不同的实体进行重要性标注以辅助算法的重要性评估. HAR [93] [95] .…”

Section: Centralityunclassified

A survey on the construction methods and applications of sci-tech big data knowledge graph

et al. 2020

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“…Reference [20] utilizes neural networks to identify high betweenness nodes. GENI [21] approximates node importance in knowledge graphs with GNNs.…”

Section: Introductionmentioning

confidence: 99%

Complex Network Metrics: Can Deep Learning Keep up With Tailor-Made Reference Algorithms?

Wandelt

Shi²,

Sun

2020

IEEE Access

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Complex network metrics are used for ranking the importance of nodes in many different applications, e.g., spreader identification and percolation analysis. Examples for such node metrics include the degree centrality and betweenness centrality. The computation of some of these metrics is computationally expensive, e.g. the computation of betweenness takes O(N 3) of computation steps in the worst case, where N is the number of nodes. In this study, we investigate the ability of deep learning via graph embedding to predict node centrality metrics in complex networks. Our study reveals that deep learning can identify vital nodes well for several of these metrics. Compared to exact computation with prohibitive costs, deep learning can get significant speedups, which scale up well with the size of the network. Further investigation on betweenness centrality reveals that the accuracy of deep learning is not as good as some tailor-made, tuned approximation algorithms. However, deep learning offers a nice trade-off between runtime and quality with its linear computational complexity regarding the network size, which makes it scalable on large networks and especially for these metrics with expensive computational costs. Our work is the first to explore the ability of deep learning on a wide range of networks metrics, and will hopefully induce future work on improved graph embeddings, tuned for specific network metrics. INDEX TERMS Complex networks, network metrics, deep learning.

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Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks

Cited by 132 publications

References 15 publications

MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals

MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals

A survey on the construction methods and applications of sci-tech big data knowledge graph

Complex Network Metrics: Can Deep Learning Keep up With Tailor-Made Reference Algorithms?

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